With the rapid development of information and communication technologies, the information on the web has grown explosively. Accordingly, a large number of researches are underway to accurately identify and provide the information users want. In particular, for activating commerce in the field of e-commerce, researches for a recommendation system, which provides the users the appropriate product information based on the user's preferences, have become an important issue.
Accurately analyzing the user preferences is essential so as to provide accurate and effective recommendation information in the recommendation system. How to analyze the user preferences can include an explicit analysis and an implicit analysis. The explicit analysis method obtains information about the user preferences by analyzing the user information or product evaluation inputted by the user. The explicit analysis has an advantage of getting information directly from the user so that the analyzing of the user preferences is faster, but it inconveniences the user. Moreover, if the user does not answer to queries correctly, it can decrease the accuracy.
The implicit analysis is a method that infers the user preferences through a variety of information about the user's behavior, and thus it is preferable to the explicit analysis since it can make an analysis without inconveniencing the user. However, the problem is that it requires more time for the analysis since it needs to gather sufficient amount of information about the user's behavior for a correct analysis.
Recently, a combined form of analysis, which mixes the above two analytical methods, has been commercially available to analyze the user preferences. That is, when the user initially visits a store, the user' behavior will be monitored by explicitly inquiring what the user is interested in and then by analyzing the user preferences based on the user's behavior, effectively reducing the time taken to analyze the user preferences with the implicit analysis. However, this analytical method also requires the user's explicit answers to the queries, thereby causing inconvenience to the user.
In addition, most of the existing recommendation systems only consider a single overall assessment item on products when analyzing the user's preferred products based on the user's behavior information. Therefore, it is difficult to determine whether it is relevant to why the user prefers a particular product.